Merge branch 'add_by_scalar' of https://github.com/antonwolfy/dpnp into add_by_scalar

Author: antonwolfy

dpnp/backend/include/dpnp_gen_2arg_3type_tbl.hpp

@@ -111,8 +111,8 @@
 
 MACRO_2ARG_3TYPES_OP(dpnp_add_c,
                      input1_elem + input2_elem,
-                     sycl::add_sat(x1, x2),
-                     MACRO_UNPACK_TYPES(int, long),
+                     nullptr,
+                     std::false_type,
                      oneapi::mkl::vm::add,
                      MACRO_UNPACK_TYPES(float, double, std::complex<float>, std::complex<double>))
 

dpnp/dpnp_array.py

@@ -140,7 +140,10 @@ def __bool__(self):
         return self._array_obj.__bool__()
 
  # '__class__',
- # '__complex__',
+ 
+    def __complex__(self):
+        return self._array_obj.__complex__()
+
  # '__contains__',
  # '__copy__',
  # '__deepcopy__',
@@ -187,7 +190,10 @@ def __gt__(self, other):
  # '__imatmul__',
  # '__imod__',
  # '__imul__',
- # '__index__',
+ 
+    def __index__(self):
+        return self._array_obj.__index__()
+
  # '__init__',
  # '__init_subclass__',
 

dpnp/dpnp_iface_arraycreation.py

@@ -48,6 +48,7 @@
 from dpnp.dpnp_utils import *
 
 import dpnp.dpnp_container as dpnp_container
+import dpctl.tensor as dpt
 
 
 __all__ = [
@@ -530,7 +531,7 @@ def empty_like(x1,
 
     Limitations
     -----------
-    Parameters ``x1`` is supported only as :class:`dpnp.dpnp_array`.
+    Parameter ``x1`` is supported as :class:`dpnp.dpnp_array` or :class:`dpctl.tensor.usm_ndarray`
     Parameter ``order`` is supported with values ``"C"`` or ``"F"``.
     Parameter ``subok`` is supported only with default value ``False``.
     Otherwise the function will be executed sequentially on CPU.
@@ -552,7 +553,7 @@ def empty_like(x1,
 
     """
 
-    if not isinstance(x1, dpnp.ndarray):
+    if not isinstance(x1, (dpnp.ndarray, dpt.usm_ndarray)):
         pass
     elif order not in ('C', 'c', 'F', 'f', None):
         pass
@@ -762,7 +763,7 @@ def full_like(x1,
 
     Limitations
     -----------
-    Parameters ``x1`` is supported only as :class:`dpnp.dpnp_array`.
+    Parameter ``x1`` is supported as :class:`dpnp.dpnp_array` or :class:`dpctl.tensor.usm_ndarray`
     Parameter ``order`` is supported only with values ``"C"`` and ``"F"``.
     Parameter ``subok`` is supported only with default value ``False``.
     Otherwise the function will be executed sequentially on CPU.
@@ -783,7 +784,7 @@ def full_like(x1,
     [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
 
     """
-    if not isinstance(x1, dpnp.ndarray):
+    if not isinstance(x1, (dpnp.ndarray, dpt.usm_ndarray)):
         pass
     elif order not in ('C', 'c', 'F', 'f', None):
         pass
@@ -1189,7 +1190,7 @@ def ones_like(x1,
 
     Limitations
     -----------
-    Parameters ``x1`` is supported only as :class:`dpnp.dpnp_array`.
+    Parameter ``x1`` is supported as :class:`dpnp.dpnp_array` or :class:`dpctl.tensor.usm_ndarray`
     Parameter ``order`` is supported with values ``"C"`` or ``"F"``.
     Parameter ``subok`` is supported only with default value ``False``.
     Otherwise the function will be executed sequentially on CPU.
@@ -1211,7 +1212,7 @@ def ones_like(x1,
     [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
 
     """
-    if not isinstance(x1, dpnp.ndarray):
+    if not isinstance(x1, (dpnp.ndarray, dpt.usm_ndarray)):
         pass
     elif order not in ('C', 'c', 'F', 'f', None):
         pass
@@ -1502,7 +1503,7 @@ def zeros_like(x1,
 
     Limitations
     -----------
-    Parameters ``x1`` is supported only as :class:`dpnp.dpnp_array`.
+    Parameter ``x1`` is supported as :class:`dpnp.dpnp_array` or :class:`dpctl.tensor.usm_ndarray`
     Parameter ``order`` is supported with values ``"C"`` or ``"F"``.
     Parameter ``subok`` is supported only with default value ``False``.
     Otherwise the function will be executed sequentially on CPU.
@@ -1523,8 +1524,8 @@ def zeros_like(x1,
     >>> [i for i in np.zeros_like(x)]
     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
 
-"""
-    if not isinstance(x1, dpnp.ndarray):
+    """
+    if not isinstance(x1, (dpnp.ndarray, dpt.usm_ndarray)):
         pass
     elif order not in ('C', 'c', 'F', 'f', None):
         pass

dpnp/random/dpnp_random_state.py

@@ -337,7 +337,7 @@ def randint(self, low, high=None, size=None, dtype=int, usm_type="device"):
         if not use_origin_backend(low):
             if not dpnp.isscalar(low):
                 pass
-            elif not dpnp.isscalar(high):
+            elif not (high is None or dpnp.isscalar(high)):
                 pass
             else:
                 _dtype = dpnp.int32 if dtype is int else dpnp.dtype(dtype)

tests/skipped_tests.tbl

@@ -769,6 +769,10 @@ tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNu
 tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_10_{name='remainder', nargs=2}::test_raises_with_numpy_input
 tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_11_{name='mod', nargs=2}::test_raises_with_numpy_input
 tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_1_{name='angle', nargs=1}::test_raises_with_numpy_input
+tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_4_{name='divide', nargs=2}::test_raises_with_numpy_input
+tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_5_{name='power', nargs=2}::test_raises_with_numpy_input
+tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_6_{name='subtract', nargs=2}::test_raises_with_numpy_input
+tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_7_{name='true_divide', nargs=2}::test_raises_with_numpy_input
 tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_8_{name='floor_divide', nargs=2}::test_raises_with_numpy_input
 tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_9_{name='fmod', nargs=2}::test_raises_with_numpy_input
 tests/third_party/cupy/math_tests/test_arithmetic.py::TestBoolSubtract_param_3_{shape=(), xp=dpnp}::test_bool_subtract

tests/test_arraycreation.py

@@ -485,3 +485,26 @@ def test_ones_like(array, dtype, order):
     a = numpy.array(array)
     ia = dpnp.array(array)
     assert_array_equal(func(numpy, a), func(dpnp, ia))
+
+
+@pytest.mark.parametrize(
+    "func, args",
+    [
+        pytest.param("full_like",
+                     ['x0', '4']),
+        pytest.param("zeros_like",
+                     ['x0']),
+        pytest.param("ones_like",
+                     ['x0']),
+        pytest.param("empty_like",
+                     ['x0']),
+    ])
+def test_dpctl_tensor_input(func, args):
+    x0 = dpt.reshape(dpt.arange(9), (3,3))
+    new_args = [eval(val, {'x0' : x0}) for val in args]
+    X = getattr(dpt, func)(*new_args)
+    Y = getattr(dpnp, func)(*new_args)
+    if func is 'empty_like':
+        assert X.shape == Y.shape
+    else:
+        assert_array_equal(X, Y)

tests/test_dparray.py

@@ -1,15 +1,17 @@
-import dpnp
-import numpy
 import pytest
+from .helper import get_all_dtypes
+
+import dpnp
 import dpctl.tensor as dpt
 
+import numpy
+from numpy.testing import (
+    assert_array_equal
+)
+
 
-@pytest.mark.parametrize("res_dtype",
-                         [numpy.float64, numpy.float32, numpy.int64, numpy.int32, numpy.bool_, numpy.complex_],
-                         ids=['float64', 'float32', 'int64', 'int32', 'bool', 'complex'])
-@pytest.mark.parametrize("arr_dtype",
-                         [numpy.float64, numpy.float32, numpy.int64, numpy.int32, numpy.bool_, numpy.complex_],
-                         ids=['float64', 'float32', 'int64', 'int32', 'bool', 'complex'])
+@pytest.mark.parametrize("res_dtype", get_all_dtypes())
+@pytest.mark.parametrize("arr_dtype", get_all_dtypes())
 @pytest.mark.parametrize("arr",
                          [[-2, -1, 0, 1, 2], [[-2, -1], [1, 2]], []],
                          ids=['[-2, -1, 0, 1, 2]', '[[-2, -1], [1, 2]]', '[]'])
@@ -18,12 +20,10 @@ def test_astype(arr, arr_dtype, res_dtype):
     dpnp_array = dpnp.array(numpy_array)
     expected = numpy_array.astype(res_dtype)
     result = dpnp_array.astype(res_dtype)
-    numpy.testing.assert_array_equal(expected, result)
+    assert_array_equal(expected, result)
 
 
-@pytest.mark.parametrize("arr_dtype",
-                         [numpy.float64, numpy.float32, numpy.int64, numpy.int32, numpy.bool_, numpy.complex_],
-                         ids=['float64', 'float32', 'int64', 'int32', 'bool', 'complex'])
+@pytest.mark.parametrize("arr_dtype", get_all_dtypes())
 @pytest.mark.parametrize("arr",
                          [[-2, -1, 0, 1, 2], [[-2, -1], [1, 2]], []],
                          ids=['[-2, -1, 0, 1, 2]', '[[-2, -1], [1, 2]]', '[]'])
@@ -32,7 +32,7 @@ def test_flatten(arr, arr_dtype):
     dpnp_array = dpnp.array(arr, dtype=arr_dtype)
     expected = numpy_array.flatten()
     result = dpnp_array.flatten()
-    numpy.testing.assert_array_equal(expected, result)
+    assert_array_equal(expected, result)
 
 
 @pytest.mark.parametrize("shape",
@@ -68,3 +68,29 @@ def test_flags_strides(dtype, order, strides):
     assert usm_array.flags == dpnp_array.flags
     assert numpy_array.flags.c_contiguous == dpnp_array.flags.c_contiguous
     assert numpy_array.flags.f_contiguous == dpnp_array.flags.f_contiguous
+
+
+@pytest.mark.parametrize("func", [bool, float, int, complex])
+@pytest.mark.parametrize("shape", [tuple(), (1,), (1, 1), (1, 1, 1)])
+@pytest.mark.parametrize("dtype", get_all_dtypes(no_float16=False, no_complex=True))
+def test_scalar_type_casting(func, shape, dtype):
+    numpy_array = numpy.full(shape, 5, dtype=dtype)
+    dpnp_array = dpnp.full(shape, 5, dtype=dtype)
+    assert func(numpy_array) == func(dpnp_array)
+
+
+@pytest.mark.parametrize("method", ["__bool__", "__float__", "__int__", "__complex__"])
+@pytest.mark.parametrize("shape", [tuple(), (1,), (1, 1), (1, 1, 1)])
+@pytest.mark.parametrize("dtype", get_all_dtypes(no_float16=False, no_complex=True, no_none=True))
+def test_scalar_type_casting_by_method(method, shape, dtype):
+    numpy_array = numpy.full(shape, 4.7, dtype=dtype)
+    dpnp_array = dpnp.full(shape, 4.7, dtype=dtype)
+    assert getattr(numpy_array, method)() == getattr(dpnp_array, method)()
+
+
+@pytest.mark.parametrize("shape", [(1,), (1, 1), (1, 1, 1)])
+@pytest.mark.parametrize("index_dtype", [dpnp.int32, dpnp.int64])
+def test_array_as_index(shape, index_dtype):
+    ind_arr = dpnp.ones(shape, dtype=index_dtype)
+    a = numpy.arange(ind_arr.size + 1)
+    assert a[tuple(ind_arr)] == a[1]

tests/third_party/cupy/random_tests/test_sample.py

@@ -33,7 +33,6 @@ def test_lo_hi_nonrandom(self):
         a = random.randint(-1.1, -0.9, size=(2, 2))
         numpy.testing.assert_array_equal(a, cupy.full((2, 2), -1))
 
-    @pytest.mark.usefixtures("allow_fall_back_on_numpy")
     def test_zero_sizes(self):
         a = random.randint(10, size=(0,))
         numpy.testing.assert_array_equal(a, cupy.array(()))
@@ -112,7 +111,6 @@ def test_goodness_of_fit_2(self):
         self.assertTrue(hypothesis.chi_square_test(counts, expected))
 
 
-@pytest.mark.usefixtures("allow_fall_back_on_numpy")
 @testing.gpu
 class TestRandintDtype(unittest.TestCase):